Overview

Dataset statistics

Number of variables14
Number of observations346213
Missing cells54115
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.0 MiB
Average record size in memory112.0 B

Variable types

Categorical1
DateTime3
Numeric9
Text1

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
GEMIDDELDE_VERKOOPPRIJS has 54115 (15.6%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.44392654)Skewed

Reproduction

Analysis started2023-12-07 16:07:35.117739
Analysis finished2023-12-07 16:07:51.525223
Duration16.41 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1.0
346213 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1038639
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 346213
100.0%

Length

2023-12-07T16:07:51.616808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-07T16:07:51.753828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 346213
100.0%

Most occurring characters

ValueCountFrequency (%)
1 346213
33.3%
. 346213
33.3%
0 346213
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 692426
66.7%
Other Punctuation 346213
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 346213
50.0%
0 346213
50.0%
Other Punctuation
ValueCountFrequency (%)
. 346213
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1038639
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 346213
33.3%
. 346213
33.3%
0 346213
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1038639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 346213
33.3%
. 346213
33.3%
0 346213
33.3%

DATUM_BESTAND
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2023-11-20 00:00:00
Maximum2023-11-20 00:00:00
2023-12-07T16:07:51.868355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:52.002865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

PEILDATUM
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2023-11-01 00:00:00
Maximum2023-11-01 00:00:00
2023-12-07T16:07:52.267344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:52.400232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

JAAR
Date

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2012-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-12-07T16:07:52.525582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:52.675656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.02967
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:52.845221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1038.9564
Coefficient of variation (CV)2.3035212
Kurtosis54.718454
Mean451.02967
Median Absolute Deviation (MAD)8
Skewness7.5263612
Sum1.5615234 × 108
Variance1079430.5
MonotonicityNot monotonic
2023-12-07T16:07:53.036376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 48557
14.0%
313 44934
13.0%
303 39831
11.5%
330 27329
 
7.9%
316 23541
 
6.8%
308 18724
 
5.4%
306 14541
 
4.2%
324 14202
 
4.1%
301 13845
 
4.0%
304 11282
 
3.3%
Other values (18) 89427
25.8%
ValueCountFrequency (%)
301 13845
 
4.0%
302 7624
 
2.2%
303 39831
11.5%
304 11282
 
3.3%
305 48557
14.0%
306 14541
 
4.2%
307 6072
 
1.8%
308 18724
 
5.4%
310 3779
 
1.1%
313 44934
13.0%
ValueCountFrequency (%)
8418 4680
 
1.4%
8416 1100
 
0.3%
1900 229
 
0.1%
390 954
 
0.3%
389 3622
 
1.0%
362 4447
 
1.3%
361 2517
 
0.7%
335 3495
 
1.0%
330 27329
7.9%
329 905
 
0.3%
Distinct1902
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:53.441836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3528204
Min length2

Characters and Unicode

Total characters1160790
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowF21
2nd rowM16
3rd rowB15
4th rowK24
5th rowG23
ValueCountFrequency (%)
101 1481
 
0.4%
402 1422
 
0.4%
403 1396
 
0.4%
301 1392
 
0.4%
201 1321
 
0.4%
203 1290
 
0.4%
401 1166
 
0.3%
404 1152
 
0.3%
409 1124
 
0.3%
802 1122
 
0.3%
Other values (1892) 333347
96.3%
2023-12-07T16:07:54.065988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 221967
19.1%
0 213288
18.4%
2 153832
13.3%
3 125578
10.8%
5 89585
7.7%
9 83562
 
7.2%
4 82225
 
7.1%
7 68396
 
5.9%
6 60608
 
5.2%
8 50020
 
4.3%
Other values (15) 11729
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1149061
99.0%
Uppercase Letter 11729
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2187
18.6%
M 1972
16.8%
B 1424
12.1%
Z 1014
8.6%
E 984
8.4%
D 769
 
6.6%
A 761
 
6.5%
F 729
 
6.2%
C 386
 
3.3%
K 379
 
3.2%
Other values (5) 1124
9.6%
Decimal Number
ValueCountFrequency (%)
1 221967
19.3%
0 213288
18.6%
2 153832
13.4%
3 125578
10.9%
5 89585
7.8%
9 83562
 
7.3%
4 82225
 
7.2%
7 68396
 
6.0%
6 60608
 
5.3%
8 50020
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1149061
99.0%
Latin 11729
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2187
18.6%
M 1972
16.8%
B 1424
12.1%
Z 1014
8.6%
E 984
8.4%
D 769
 
6.6%
A 761
 
6.5%
F 729
 
6.2%
C 386
 
3.3%
K 379
 
3.2%
Other values (5) 1124
9.6%
Common
ValueCountFrequency (%)
1 221967
19.3%
0 213288
18.6%
2 153832
13.4%
3 125578
10.9%
5 89585
7.8%
9 83562
 
7.3%
4 82225
 
7.2%
7 68396
 
6.0%
6 60608
 
5.3%
8 50020
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1160790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 221967
19.1%
0 213288
18.4%
2 153832
13.3%
3 125578
10.8%
5 89585
7.7%
9 83562
 
7.2%
4 82225
 
7.1%
7 68396
 
5.9%
6 60608
 
5.2%
8 50020
 
4.3%
Other values (15) 11729
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6196
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4118692 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:54.294422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199799067
median1.4959903 × 108
Q39.90004 × 108
95-th percentile9.9051605 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020494 × 108

Descriptive statistics

Standard deviation4.2906031 × 108
Coefficient of variation (CV)0.97251366
Kurtosis-1.7391839
Mean4.4118692 × 108
Median Absolute Deviation (MAD)1.1960003 × 108
Skewness0.46571714
Sum1.5274465 × 1014
Variance1.8409275 × 1017
MonotonicityNot monotonic
2023-12-07T16:07:54.511729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2534
 
0.7%
990004007 2488
 
0.7%
990003004 2394
 
0.7%
990004006 2021
 
0.6%
990356076 1850
 
0.5%
990356073 1718
 
0.5%
131999228 1696
 
0.5%
131999164 1670
 
0.5%
990003007 1549
 
0.4%
131999194 1524
 
0.4%
Other values (6186) 326769
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 12
< 0.1%
10501004 12
< 0.1%
10501005 12
< 0.1%
10501007 3
 
< 0.1%
10501008 12
< 0.1%
10501010 12
< 0.1%
10501011 3
 
< 0.1%
11101002 11
< 0.1%
11101003 12
< 0.1%
ValueCountFrequency (%)
998418081 176
0.1%
998418080 160
< 0.1%
998418079 40
 
< 0.1%
998418077 9
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 240
0.1%
998418073 241
0.1%
998418072 9
 
< 0.1%
998418071 9
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

Distinct10449
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean510.89698
Minimum1
Maximum165184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:54.713212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q3102
95-th percentile1725
Maximum165184
Range165183
Interquartile range (IQR)99

Descriptive statistics

Standard deviation3178.1465
Coefficient of variation (CV)6.220719
Kurtosis416.34993
Mean510.89698
Median Absolute Deviation (MAD)12
Skewness16.880538
Sum1.7687918 × 108
Variance10100615
MonotonicityNot monotonic
2023-12-07T16:07:54.919469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 57494
 
16.6%
2 28055
 
8.1%
3 18371
 
5.3%
4 13374
 
3.9%
5 10511
 
3.0%
6 8842
 
2.6%
7 7418
 
2.1%
8 6180
 
1.8%
9 5682
 
1.6%
10 5087
 
1.5%
Other values (10439) 185199
53.5%
ValueCountFrequency (%)
1 57494
16.6%
2 28055
8.1%
3 18371
 
5.3%
4 13374
 
3.9%
5 10511
 
3.0%
6 8842
 
2.6%
7 7418
 
2.1%
8 6180
 
1.8%
9 5682
 
1.6%
10 5087
 
1.5%
ValueCountFrequency (%)
165184 1
< 0.1%
162461 1
< 0.1%
158301 1
< 0.1%
155869 1
< 0.1%
154539 1
< 0.1%
154258 1
< 0.1%
144714 1
< 0.1%
118396 1
< 0.1%
115935 1
< 0.1%
113248 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

SKEWED 

Distinct11205
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.51161
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:55.122863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3111
95-th percentile1972
Maximum240002
Range240001
Interquartile range (IQR)108

Descriptive statistics

Standard deviation4102.4335
Coefficient of variation (CV)6.7751525
Kurtosis729.15346
Mean605.51161
Median Absolute Deviation (MAD)13
Skewness21.443927
Sum2.0963599 × 108
Variance16829961
MonotonicityNot monotonic
2023-12-07T16:07:55.333138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 55381
 
16.0%
2 27571
 
8.0%
3 18184
 
5.3%
4 13180
 
3.8%
5 10421
 
3.0%
6 8810
 
2.5%
7 7344
 
2.1%
8 6136
 
1.8%
9 5608
 
1.6%
10 5081
 
1.5%
Other values (11195) 188497
54.4%
ValueCountFrequency (%)
1 55381
16.0%
2 27571
8.0%
3 18184
 
5.3%
4 13180
 
3.8%
5 10421
 
3.0%
6 8810
 
2.5%
7 7344
 
2.1%
8 6136
 
1.8%
9 5608
 
1.6%
10 5081
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
230939 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226233 1
< 0.1%
223891 1
< 0.1%
218673 1
< 0.1%
215131 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

Distinct9408
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7661.0515
Minimum1
Maximum230662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:55.528061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40
Q1395
median1693
Q36243
95-th percentile36749
Maximum230662
Range230661
Interquartile range (IQR)5848

Descriptive statistics

Standard deviation17896.73
Coefficient of variation (CV)2.336067
Kurtosis35.029884
Mean7661.0515
Median Absolute Deviation (MAD)1548
Skewness5.120952
Sum2.6523556 × 109
Variance3.2029293 × 108
MonotonicityNot monotonic
2023-12-07T16:07:55.727099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 613
 
0.2%
17 523
 
0.2%
8 522
 
0.2%
26 509
 
0.1%
25 508
 
0.1%
9 502
 
0.1%
12 501
 
0.1%
19 491
 
0.1%
14 489
 
0.1%
23 477
 
0.1%
Other values (9398) 341078
98.5%
ValueCountFrequency (%)
1 402
0.1%
2 464
0.1%
3 476
0.1%
4 450
0.1%
5 477
0.1%
6 447
0.1%
7 458
0.1%
8 522
0.2%
9 502
0.1%
10 411
0.1%
ValueCountFrequency (%)
230662 23
< 0.1%
229208 20
< 0.1%
227999 23
< 0.1%
218495 24
< 0.1%
214507 17
< 0.1%
213515 25
< 0.1%
211579 17
< 0.1%
210415 19
< 0.1%
205337 17
< 0.1%
200600 16
< 0.1%
Distinct10496
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11107.437
Minimum1
Maximum370139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:55.918848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51
Q1523
median2368
Q39066
95-th percentile52146
Maximum370139
Range370138
Interquartile range (IQR)8543

Descriptive statistics

Standard deviation26862.034
Coefficient of variation (CV)2.4183828
Kurtosis38.366335
Mean11107.437
Median Absolute Deviation (MAD)2178
Skewness5.3546399
Sum3.845539 × 109
Variance7.2156889 × 108
MonotonicityNot monotonic
2023-12-07T16:07:56.126684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 442
 
0.1%
25 436
 
0.1%
23 424
 
0.1%
3 410
 
0.1%
4 397
 
0.1%
20 397
 
0.1%
6 396
 
0.1%
10 391
 
0.1%
13 387
 
0.1%
34 387
 
0.1%
Other values (10486) 342146
98.8%
ValueCountFrequency (%)
1 317
0.1%
2 337
0.1%
3 410
0.1%
4 397
0.1%
5 368
0.1%
6 396
0.1%
7 364
0.1%
8 355
0.1%
9 293
0.1%
10 391
0.1%
ValueCountFrequency (%)
370139 23
< 0.1%
365382 23
< 0.1%
350959 20
< 0.1%
348482 25
< 0.1%
344610 24
< 0.1%
341651 19
< 0.1%
323753 20
< 0.1%
315771 17
< 0.1%
310754 17
< 0.1%
298627 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean665955.03
Minimum1610
Maximum1487632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:56.334631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1610
5-th percentile41749
Q1260836
median757846
Q31026293
95-th percentile1332313
Maximum1487632
Range1486022
Interquartile range (IQR)765457

Descriptive statistics

Standard deviation418497.13
Coefficient of variation (CV)0.6284165
Kurtosis-1.1773654
Mean665955.03
Median Absolute Deviation (MAD)319233
Skewness0.011544625
Sum2.3056229 × 1011
Variance1.7513984 × 1011
MonotonicityNot monotonic
2023-12-07T16:07:56.546553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880928 5102
 
1.5%
874088 4354
 
1.3%
843978 4347
 
1.3%
894305 4333
 
1.3%
880465 4273
 
1.2%
897698 4212
 
1.2%
765015 4089
 
1.2%
803555 4028
 
1.2%
781731 3993
 
1.2%
1080721 3890
 
1.1%
Other values (315) 303592
87.7%
ValueCountFrequency (%)
1610 130
 
< 0.1%
1829 138
 
< 0.1%
1920 131
 
< 0.1%
2495 173
< 0.1%
2553 190
0.1%
2862 102
 
< 0.1%
4140 168
< 0.1%
5156 68
 
< 0.1%
6117 331
0.1%
6805 380
0.1%
ValueCountFrequency (%)
1487632 2975
0.9%
1450390 3048
0.9%
1421702 3564
1.0%
1344256 3543
1.0%
1340565 3441
1.0%
1332313 3545
1.0%
1316350 3463
1.0%
1282933 3576
1.0%
1278266 3370
1.0%
1267046 3350
1.0%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1080927.3
Minimum1861
Maximum2664173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:56.759867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile46341
Q1378547
median1087900
Q31808514
95-th percentile2548166
Maximum2664173
Range2662312
Interquartile range (IQR)1429967

Descriptive statistics

Standard deviation753404.17
Coefficient of variation (CV)0.69699799
Kurtosis-0.829534
Mean1080927.3
Median Absolute Deviation (MAD)716868
Skewness0.3690758
Sum3.7423109 × 1011
Variance5.6761784 × 1011
MonotonicityNot monotonic
2023-12-07T16:07:56.974574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211797 5102
 
1.5%
1281482 4354
 
1.3%
1216253 4347
 
1.3%
1315564 4333
 
1.3%
1300429 4273
 
1.2%
1341821 4212
 
1.2%
1155933 4089
 
1.2%
1205563 4028
 
1.2%
1157648 3993
 
1.2%
2548166 3890
 
1.1%
Other values (315) 303592
87.7%
ValueCountFrequency (%)
1861 130
 
< 0.1%
2097 138
 
< 0.1%
2195 131
 
< 0.1%
2816 173
< 0.1%
3052 102
 
< 0.1%
3321 190
0.1%
4946 168
< 0.1%
5427 68
 
< 0.1%
6300 331
0.1%
7384 380
0.1%
ValueCountFrequency (%)
2664173 3866
1.1%
2662911 3793
1.1%
2618563 3789
1.1%
2593432 3843
1.1%
2548166 3890
1.1%
2514582 3889
1.1%
2479950 3851
1.1%
2178503 3757
1.1%
2062152 3811
1.1%
2052147 1168
 
0.3%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3640
Distinct (%)1.2%
Missing54115
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean3594.7539
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-07T16:07:57.312174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1480
median1250
Q34175
95-th percentile13710
Maximum287220
Range287150
Interquartile range (IQR)3695

Descriptive statistics

Standard deviation6523.3583
Coefficient of variation (CV)1.8146884
Kurtosis138.49976
Mean3594.7539
Median Absolute Deviation (MAD)1020
Skewness6.9966081
Sum1.0500204 × 109
Variance42554203
MonotonicityNot monotonic
2023-12-07T16:07:57.515497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2100
 
0.6%
105 1977
 
0.6%
110 1791
 
0.5%
180 1614
 
0.5%
300 1607
 
0.5%
185 1580
 
0.5%
140 1515
 
0.4%
175 1474
 
0.4%
125 1388
 
0.4%
165 1384
 
0.4%
Other values (3630) 275668
79.6%
(Missing) 54115
 
15.6%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 663
 
0.2%
95 727
 
0.2%
100 1019
0.3%
105 1977
0.6%
110 1791
0.5%
115 1156
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
99590 5
< 0.1%

Interactions

2023-12-07T16:07:48.863418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:37.057468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:38.651414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:40.080904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:41.514479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:42.927536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:44.322740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:45.944208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:47.426195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:49.038127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:37.320687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:38.820450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:40.254168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:41.683229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:43.095031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:44.500127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:46.119488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:47.596380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:49.195963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:37.484306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:38.973304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:40.408131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:41.837134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:43.244677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:44.794360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:46.279839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:47.752519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:49.358526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:37.652441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:39.131423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:40.564600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:41.992463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:43.399822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:44.957756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:46.443461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:47.911505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:49.515039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:37.814840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:39.286242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:40.717319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:42.142713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:43.547534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:45.118163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:46.604713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:48.066776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:49.666338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:37.970588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:39.432985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:40.864450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:42.286341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:43.690137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:45.270942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:46.759599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:48.214732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:49.833574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:38.144142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:39.599373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:41.029062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:42.449557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:43.851622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:45.441260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:46.929950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:48.381874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:50.005228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:38.317025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:39.763950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:41.196860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:42.613044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:44.015292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:45.614884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:47.098767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:48.547033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:50.162947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:38.482214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:39.921748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:41.354422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:42.770243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:44.168597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:45.778809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:47.260497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-07T16:07:48.702854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-07T16:07:50.417050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-07T16:07:50.917570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02023-11-202023-11-012021-01-01307F2197280402236044439253718280971632811962591510.0
11.02023-11-202023-11-012021-01-01307M1620108156231125829108177163281196259835.0
21.02023-11-202023-11-012021-01-01307B1515989900422247247716328119625910860.0
31.02023-11-202023-11-012021-01-01307K2415989902010541101145715747163281196259330.0
41.02023-11-202023-11-012021-01-01307G23149399026380403579937517171632811962592110.0
51.02023-11-202023-11-012021-01-01307Z28159999033752767414748237163281196259605.0
61.02023-11-202023-11-012021-01-01307F119728040242237991549897163281196259670.0
71.02023-11-202023-11-012021-01-01307M16201081813723855829108177163281196259635.0
81.02023-11-202023-11-012021-01-01307G251499990456658583903247163281196259NaN
91.02023-11-202023-11-012021-01-01307K25598990082219207163281196259NaN
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3462031.02023-11-202023-11-012014-01-01303841990356051221116142170218456153840.0
3462041.02023-11-202023-11-012015-01-013622219900620152246349270900815971450.0
3462051.02023-11-202023-11-012020-01-0131307997900231111266210469612618563NaN
3462061.02023-11-202023-11-012014-01-013038561493990401191114217021845615220.0
3462071.02023-11-202023-11-012020-01-013137629790030092320318441046961261856322785.0
3462081.02023-11-202023-11-012020-01-013207019790012234452188393103095918310466655.0
3462091.02023-11-202023-11-012020-01-0131307897900231411332870104696126185634845.0
3462101.02023-11-202023-11-012014-01-013032221992990681176669519142170218456154640.0
3462111.02023-11-202023-11-012014-01-01303293131999214221313177714217021845615340.0
3462121.02023-11-202023-11-012014-01-0130328813199909911222271142170218456151940.0